Metamodel-Based Reliability Analysis in Spatially Variable Soils Using Convolutional Neural Networks

In recent years, the random field finite-element method (FEM) has been used increasingly in geotechnical engineering to carry out analyses that account for the inherent spatial variability in the physical and mechanical properties of both natural and treated soils. However, this method, which usually is performed in tandem with Monte Carlo simulation (MCS), requires significantly greater computational resources than deterministic finite-element analysis. Metamodeling is one of the techniques commonly adopted to alleviate the computational burden. This paper proposes a novel and computationally efficient metamodeling technique that involves the use of convolutional neural networks (CNNs) to perform random field finite-element analyses. CNNs, which treat random fields as images, are capable of outputting FEM predicted quantities with learned high-level features that contain information about the random variabilities in both spatial distribution and intensity. CNNs, after being trained with sufficient random field samples, could be used as a metamodel to replace the expensive random field finite-element simulations for all subsequent calculations. The validity of the proposed approach was illustrated using a synthetic excavation problem and a synthetic surface footing problem. The good agreement between the CNN outputs and the FEM predictions demonstrated the promising potential of using CNNs as a metamodel for reliability analysis in spatially variable soils. (c) 2021 American Society of Civil Engineers.

Automated summary

The paper proposes a novel and computationally efficient metamodeling technique using convolutional neural networks for random field finite-element analyses, showing promising potential for reliability analysis in spatially variable soils. The CNN outputs demonstrated good agreement with FEM predictions, indicating the effectiveness of using CNNs as metamodels to replace expensive simulations.